cleanup code

demo/rank/mq2008.conf

@@ -1,29 +1,25 @@
 # General Parameters, see comment for each definition
 # choose the tree booster, 0: tree, 1: linear
 booster_type = 0 
-# this is the only difference with classification, use 0: linear regression
-# when labels are in [0,1] we can also use 1: logistic regression
-loss_type = 0
 
 #objective="rank:pairwise"
 #objective="rank:softmax"
 #objective="lambdarank:map"
 #objective="lambdarank:ndcg"
 
-num_feature=50
 # Tree Booster Parameters
 # step size shrinkage
-bst:eta = 1.0 
+bst:eta = 0.1 
 # minimum loss reduction required to make a further partition
 bst:gamma = 1.0 
 # minimum sum of instance weight(hessian) needed in a child
 bst:min_child_weight = 1 
 # maximum depth of a tree
 bst:max_depth = 3 
-
+eval_metric='ndcg'
 # Task parameters
 # the number of round to do boosting
-num_round = 2
+num_round = 4
 # 0 means do not save any model except the final round model
 save_period = 0 
 # The path of training data